Network bandwidth consumption is rising at a rapid rate. Existing network capacity is marginally adequate and is expected, as of this writing, to soon be inadequate. Thus, there is a need to increase network bandwidth capacity. This increase in bandwidth can be achieved by increasing the symbol transmission rate to yield a corresponding increase in the data rate or by using advanced modulation techniques with higher spectral efficiency (i.e. techniques that communicate more than one information bit per symbol).
Regardless of the technique employed to achieve higher data throughput, the higher data throughput can place more stringent requirements on the fidelity of the signal communicated. Fidelity of the signal communicated can be hampered by signal degradation. Signal degradation can occur during signal generation and signal transmission. Signal degradation incurred in generating and transmitting a signal over a channel can largely be categorized as arising from two sources: (i) filtering of the signal and (ii) corruption from noise.
In classical communications (e.g. wireless or wireline communications), the noise component is commonly addressed by using optimal detection (i.e. matched-filtering followed by optimal thresholding). However, such a conventional approach often neglects the inter-symbol interference (ISI) associated with the filtering that occurs in the channel, i.e. that approach assumes that the noise is the dominant source of distortion. If the ISI is the dominant source of signal degradation, then the conventional approach is to equalize the channel, e.g. filter the received signal with an inverse filter prior to detection. The use of any one of these approaches in isolation may not improve signal fidelity since matched-filtering and equalization are often contradicting goals.
For example, equalization generally corresponds to high-pass filtering which, while removing ISI, increases the presence of high-frequency noise. A low-pass filter (LPF) is usually employed to the equalized signal in order to reduce the effect of the high-frequency noise but which also re-introduces ISI. Matched-filtering, on the other hand, is often low-pass in nature and thus frequently exacerbates the ISI in the signal in the process of reducing noise.
The separate application of matched-filtering and equalization can be characterized as “ad-hoc” because it does not consider the problem of noise mitigation and equalization in a combined framework, and thus, neglects the impact each has on the other.
There exist techniques in the conventional art which address noise mitigation and equalization in a common framework. In particular, the well-known Least-Mean Squares (LMS) based approaches minimize a distortion measure that captures the impact of both noise and ISI. Furthermore, these methods are adaptive in the sense that the settings of the filter are automatically adjusted to the optimal value. This adaptive feature is often necessary as the exact characteristics of the channel distortion and noise spectral content vary from installation to installation and also with time and temperature in some instances.
Unfortunately, the use of these traditional adaptive LMS-based control methodologies for high-data rate systems can be impractical due to data acquisition and processing difficulties. In particular, it can be economically impractical (and often technically infeasible) to (i) produce the analog-to-digital converters (ADC's) capable of digitizing the signal at the required speed and resolution and (ii) produce a processor capable of handling the digitized data at the high speeds.
Therefore, there is a need in the art for an adaptive filtering approach that combines channel equalization and noise filtering. Another need exists in the art for a method and system for high speed digital communications that combines channel equalization and noise filtering in a single framework and that can account for the effects that equalization can have on noise filtering, and vice-versa. Additionally, there is a need for such a method and system which is economically and technically practical for high-speed data systems.